⚗️ distilabeled Marcoro14 7B Slerp

Introduction

This model is a new DPO fine-tune of our new open dataset argilla/distilabel-intel-orca-dpo-pairs, on the mlabonne/Marcoro14-7B-slerp model. You can find more information of the "distilabeled" dataset used at this repo argilla/distilabeled-Hermes-2.5-Mistral-7B, and visit distilabel.

The difference between this model and argilla/distilabeled-Marcoro14-7B-slerp is that this model has been fine-tuned for a whole epoch instead instead of 200 steps, so it has seen the whole dataset.

Training details

As we did with Notus, we wanted a reproducible recipe to test the impact of data quality.

And we're lucky to have so many amazing folks in the open community contributing reproducible, easy-to-use training scripts and recipes. This time, Maxime Labonne had shared a Colab to fine-tune OpenHermes with DPO and the original Intel's dataset, perfect! We just updated the base model to mlabonne/Marcoro14-7B-slerp, and applied the same dataset recipe we used for argilla/distilabeled-Hermes-2.5-Mistral-7B:

from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# we did this
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r: 
        r["status"] != "tie" and 
        r["chosen_score"] >= 8 and 
        not r["in_gsm8k_train"]
)

Benchmark results

For benchmarking we used the famous "Nous" or "Teknium" benchmark. You can find below an overview, including our first experiment with a less ambitious dataset filtering (removing ties and score>5).

For running the benchmark we used another awesome contribution from Maxime: LLM AutoEval, check it out!

Model	AGIEval	GPT4ALL	TruthfulQA	Bigbench	Average
argilla/distilabeled-Marcoro14-7B-slerp-full	45.17	76.59	64.68	48.15	58.65
argilla/distilabeled-Marcoro14-7B-slerp	45.4	76.47	65.46	47.19	58.63
Marcoro14-7B-slerp	44.66	76.24	64.15	45.64	57.67
argilla/distilabeled-Hermes-2.5-Mistral-7B	44.64	73.35	55.96	42.21	54.04

Training Hardware

We used 1 x A100 80GB in runpod for less than 2 hours.

Acknowledgements

We'd like to thank the amazing open community and in particular:

The Intel team for publishing a great open dataset and show how well it worked in the first place
Teknium and NousResearch for their awesome work and models.
Maxime for sharing such great resources.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.40
AI2 Reasoning Challenge (25-Shot)	70.65
HellaSwag (10-Shot)	87.55
MMLU (5-Shot)	65.33
TruthfulQA (0-shot)	64.21
Winogrande (5-shot)	82.00
GSM8k (5-shot)	70.66

argilla
/

distilabeled-Marcoro14-7B-slerp-full